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Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Kafka's assign() method allows manual control of partition assignments for consumers.

Sharding splits databases into smaller parts to improve performance and scalability.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Choosing the right boundaries for microservices is crucial for effective architecture.

Distributed caching spreads data across multiple servers for better performance and reliability.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Choosing between a shared database and a database per service affects system design significantly.

Microservices and modular monoliths differ mainly in deployment and communication methods.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Choosing the right distributed lock depends on your system's needs and existing tools.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.